A deep learning method based on prior knowledge with dual training for solving FPK equation

The evolution of the probability density function of a stochastic dynamical system over time can be described by a Fokker–Planck–Kolmogorov(FPK) equation, the solution of which determines the distribution of macroscopic variables in the stochastic dynamic system. Traditional methods for solving these equations often struggle with computational efficiency and scalability, particularly in high-dimensional contexts. To address these challenges, this paper proposes a novel deep learning method based on prior knowledge with dual training to solve the stationary FPK equations. Initially, the neural network is pre-trained through the prior knowledge obtained by Monte Carlo simulation(MCS). Subsequently, the second training phase incorporates the FPK differential operator into the loss function, while a supervisory term consisting of local maximum points is specifically included to mitigate the generation of zero solutions. This dual-training strategy not only expedites convergence but also enhances computational efficiency, making the method well-suited for high-dimensional systems. Numerical examples, including two different two-dimensional(2D), six-dimensional(6D), and eight-dimensional(8D) systems, are conducted to assess the efficacy of the proposed method. The results demonstrate robust performance in terms of both computational speed and accuracy for solving FPK equations in the first three systems. While the method is also applicable to high-dimensional systems, such as 8D, it should be noted that computational efficiency may be marginally compromised due to data volume constraints.

Cascade Human Activity Recognition Based on Simple Computations Incorporating Appropriate Prior Knowledge

The purpose of Human Activities Recognition(HAR)is to recognize human activities with sensors like accelerometers and gyroscopes.The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms.In this paper,we experimentally validate the HAR process and its various algorithms independently.On the base of which,it is further proposed that,in addition to the necessary eigenvalues and intelligent algorithms,correct prior knowledge is even more critical.The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object,the sampling process,the sampling data,the HAR algorithm,etc.Thus,a solution is presented under the guidance of right prior knowledge,using Back-Propagation neural networks(BP networks)and simple Convolutional Neural Networks(CNN).The results show that HAR can be achieved with 90%–100%accuracy.Further analysis shows that intelligent algorithms for pattern recognition and classification problems,typically represented by HAR,require correct prior knowledge to work effectively.

Dynamic Spectrum Access Based on Prior Knowledge Enabled Reinforcement Learning with Double Actions in Complex Electromagnetic Environment

The spectrum access problem of cognitive users in the fast-changing dynamic interference spectrum environment is addressed in this paper.The prior knowledge for the dynamic spectrum access is modeled and a reliability quantification scheme is presented to guide the use of the prior knowledge in the learning process.Furthermore,a spectrum access scheme based on the prior knowledge enabled RL(PKRL)is designed,which effectively improved the learning efficiency and provided a solution for users to better adapt to the fast-changing and high-density electromagnetic environment.Compared with the existing methods,the proposed algorithm can adjust the access channel online according to historical information and improve the efficiency of the algorithm to obtain the optimal access policy.Simulation results show that,the convergence speed of the learning is improved by about 66%with the invariant average throughput.

Design Hybrid Methods for Encoding Prior Knowledge in Feedforward Network with Application in Chemical Engineering

Three-layer feedforward networks have been widely used in modeling chemical engineering processes and prior-knowledge-based methods have been introduced to improve their performances.In this paper,we propose the methodology of designing better prior-knowledge-based hybrid methods by combining the existing ones. Then according to this methodology,two hybrid methods,interpolation-optimization (IO) method and interpolation penalty-function (IPF) method,are designed as examples.Finally,both methods are applied to modeling two cases in chemical engineering to investigate their effectiveness.Simulation results show that the performances of the hybrid methods are better than those of their parents.

Boosting multi-features with prior knowledge for mini unmanned helicopter landmark detection

Without sufficient real training data,the data driven classification algorithms based on boosting method cannot solely be utilized to applications such as the mini unmanned helicopter landmark image detection.In this paper,we propose an approach which uses a boosting algorithm with the prior knowledge for the mini unmanned helicopter landmark image detection.The stage forward stagewise additive model of boosting is analyzed,and the approach how to combine it with the prior knowledge model is presented.The approach is then applied to landmark image detection,where the multi-features are boosted to solve a series of problems,such as rotation,noises affected,etc.Results of real flight experiments demonstrate that for small training examples the boosted learning system using prior knowledge is dramatically better than the one driven by data only.

Reasoning Disaster Chains with Bayesian Network Estimated Under Expert Prior Knowledge

With the acceleration of global climate change and urbanization,disaster chains are always connected to artificial systems like critical infrastructure.The complexity and uncertainty of the disaster chain development process and the severity of the consequences have brought great challenges to emergency decision makers.The Bayesian network(BN)was applied in this study to reason about disaster chain scenarios to support the choice of appropriate response strategies.To capture the interacting relationships among different factors,a scenario representation model of disaster chains was developed,followed by the determination of the BN structure.In deriving the conditional probability tables of the BN model,we found that,due to the lack of data and the significant uncertainty of disaster chains,parameter learning methodologies based on data or expert knowledge alone are insufficient.By integrating both sample data and expert knowledge with the maximum entropy principle,we proposed a parameter estimation algorithm under expert prior knowledge(PEUK).Taking the rainstorm disaster chain as an example,we demonstrated the superiority of the PEUK-built BN model over the traditional maximum a posterior(MAP)algorithm and the direct expert opinion elicitation method.The results also demonstrate the potential of our BN scenario reasoning paradigm to assist real-world disaster decisions.

A Structure Learning Algorithm for Bayesian Network Using Prior Knowledge

Learning structure from data is one of the most important fundamental tasks of Bayesian network research. Particularly, learning optional structure of Bayesian network is a non-deterministic polynomial-time （NP） hard problem. To solve this problem, many heuristic algorithms have been proposed, and some of them learn Bayesian network structure with the help of different types of prior knowledge. However, the existing algorithms have some restrictions on the prior knowledge, such as quality restriction and use restriction. This makes it di？cult to use the prior knowledge well in these algorithms. In this paper, we introduce the prior knowledge into the Markov chain Monte Carlo （MCMC） algorithm and propose an algorithm called Constrained MCMC （C-MCMC） algorithm to learn the structure of the Bayesian network. Three types of prior knowledge are defined： existence of parent node, absence of parent node, and distribution knowledge including the conditional probability distribution （CPD） of edges and the probability distribution （PD） of nodes. All of these types of prior knowledge are easily used in this algorithm. We conduct extensive experiments to demonstrate the feasibility and effectiveness of the proposed method C-MCMC.

Can prior knowledge help graph-based methods for keyword extraction？

Graph-based methods are one of the widely used unsupervised approaches for keyword extraction. In this approach, words are linked according to their co- occurrences within the document. Afterwards, graph-based ranking algorithms are used to rank words and those with the highest scores are selected as keywords. Although graph-based methods are effective for keyword extraction, they rank words merely based on word graph topology. In fact, we have various prior knowledge to identify how likely the words are keywords. The knowledge of words may be frequency-based, position-based, or semantic- based. In this paper, we propose to incorporate prior knowledge with graph-based methods for keyword extraction and investigate the contributions of the prior knowledge. Experiments reveal that prior knowledge can significantly improve the performance of graph-based keyword extraction. Moreover, by combining prior knowl- edge with neighborhood knowledge, in experiments we achieve the best results compared to previous graph-based methods.

Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

Physics-data coupling-driven method to predict the penetration depth into concrete targets

The projectile penetration process into concrete target is a nonlinear complex problem.With the increase ofexperiment data,the data-driven paradigm has exhibited a new feasible method to solve such complex prob-lem.However,due to poor quality of experimental data,the traditional machine learning(ML)methods,whichare driven only by experimental data,have poor generalization capabilities and limited prediction accuracy.Therefore,this study intends to exhibit a ML method fusing the prior knowledge with experiment data.The newML method can constrain the fitting to experimental data,improve the generalization ability and the predic-tion accuracy.Experimental results show that integrating domain prior knowledge can effectively improve theperformance of the prediction model for penetration depth into concrete targets.

Knowledge-based adaptive polarimetric detection in heterogeneous clutter

The detection performance and the constant false alarm rate behavior of the conventional adaptive detectors are severely degraded in heterogeneous clutter. This paper designs and analyses a knowledge-based （KB） adaptive polarimetric detector in het-erogeneous clutter. The proposed detection scheme is composed of a data selector using polarization knowledge and an adaptive polarization detector using training data. A polarization data selector based on the maximum likelihood estimation is proposed to remove outliers from the heterogeneous training data. This selector can remove outliers effectively, thus the training data is purified for estimating the clutter covariance matrix. Consequently, the performance of the adaptive detector is improved. We assess the performance of the KB adaptive polarimetric detector and the adaptive polarimetric detector without a data selector using simulated data and IPIX radar data. The results show that the KB adaptive polarization detector outperforms its non-KB counterparts.

Applications and potentials of machine learning in optoelectronic materials research:An overview and perspectives

Optoelectronic materials are essential for today's scientific and technological development,and machine learning provides new ideas and tools for their research.In this paper,we first summarize the development history of optoelectronic materials and how materials informatics drives the innovation and progress of optoelectronic materials and devices.Then,we introduce the development of machine learning and its general process in optoelectronic materials and describe the specific implementation methods.We focus on the cases of machine learning in several application scenarios of optoelectronic materials and devices,including the methods related to crystal structure,properties(defects,electronic structure)research,materials and devices optimization,material characterization,and process optimization.In summarizing the algorithms and feature representations used in different studies,it is noted that prior knowledge can improve optoelectronic materials design,research,and decision-making processes.Finally,the prospect of machine learning applications in optoelectronic materials is discussed,along with current challenges and future directions.This paper comprehensively describes the application value of machine learning in optoelectronic materials research and aims to provide reference and guidance for the continuous development of this field.

Patch-based vehicle logo detection with patch intensity and weight matrix

A patch-based method for detecting vehicle logos using prior knowledge is proposed.By representing the coarse region of the logo with the weight matrix of patch intensity and position,the proposed method is robust to bad and complex environmental conditions.The bounding-box of the logo is extracted by a thershloding approach.Experimental results show that 93.58% location accuracy is achieved with 1100 images under various environmental conditions,indicating that the proposed method is effective and suitable for the location of vehicle logo in practical applications.

A Method of Eliminating Information Disclosure in View Publishing

Although it is convenient to exchange data by publishing view, but it may disclose sensitive information. The problem of how to eliminate information disclosure becomes a core problem in the view publishing process. In order to eliminate information disclosure, deciding view security algorithm and eliminating information disclosure algorithm are proposed, and the validity of the algorithms are proved by experiment. The experimental results showing, deciding view security algorithm can decide the safety of a set of views under prior knowledge, and eliminating information disclosure algorithm can eliminate disclosure efficiently.

Bayesian Inference of Empirical Coefficient for Foundation Settlement

A new approach based on Bayesian theory is proposed to determine the empirical coefficient in soil settlement calculation. Prior distribution is assumed to he uniform in [ 0.2,1.4 ]. Posterior density function is developed in the condition of prior distribution combined with the information of observed samples at four locations on a passenger dedicated fine. The results show that the posterior distribution of the empirical coefficient obeys Gaussian distribution. The mean value of the empirical coefficient decreases gradually with the increasing of the load on ground, and variance variation shows no regularity.

Autonomous mobile robot global path planning: a prior information-based particle swarm optimization approach

The path planning of autonomous mobile robots(PPoAMR)is a very complex multi-constraint problem.The main goal is to find the shortest collision-free path from the starting point to the target point.By the fact that the PPoAMR problem has the prior knowledge that the straight path between the starting point and the target point is the optimum solution when obstacles are not considered.This paper proposes a new path planning algorithm based on the prior knowledge of PPoAMR,which includes the fitness value calculation method and the prior knowledge particle swarm optimization(PKPSO)algorithm.The new fitness calculation method can preserve the information carried by each individual as much as possible by adding an adaptive coefficient.The PKPSO algorithm modifies the particle velocity update method by adding a prior particle calculated from the prior knowledge of PPoAMR and also implemented an elite retention strategy,which improves the local optima evasion capability.In addition,the quintic polynomial trajectory optimization approach is devised to generate a smooth path.Finally,some experimental comparisons with those state-of-the-arts are carried out to demonstrate the effectiveness of the proposed path planning algorithm.

Knowledge Fusion Design Method:Satellite Module Layout

As a complex engineering problem,the satellite module layout design (SMLD) is difficult to resolve by using conventional computation-based approaches. The challenges stem from three aspects:computational complexity,engineering complexity,and engineering practicability. Engineers often finish successful satellite designs by way of their plenty of experience and wisdom,lessons learnt from the past practices,as well as the assistance of the advanced computational techniques. Enlightened by the ripe patterns,th...

Adaptive and augmented active anomaly detection on dynamic network traffic streams

Active anomaly detection queries labels of sampled instances and uses them to incrementally update the detection model,and has been widely adopted in detecting network attacks.However,existing methods cannot achieve desirable performance on dynamic network traffic streams because(1)their query strategies cannot sample informative instances to make the detection model adapt to the evolving stream and(2)their model updating relies on limited query instances only and fails to leverage the enormous unlabeled instances on streams.To address these issues,we propose an active tree based model,adaptive and augmented active prior-knowledge forest(A3PF),for anomaly detection on network trafic streams.A prior-knowledge forest is constructed using prior knowledge of network attacks to find feature subspaces that better distinguish network anomalies from normal traffic.On one hand,to make the model adapt to the evolving stream,a novel adaptive query strategy is designed to sample informative instances from two aspects:the changes in dynamic data distribution and the uncertainty of anomalies.On the other hand,based on the similarity of instances in the neighborhood,we devise an augmented update method to generate pseudo labels for the unlabeled neighbors of query instances,which enables usage of the enormous unlabeled instances during model updating.Extensive experiments on two benchmarks,CIC-IDS2017 and UNSW-NB15,demonstrate that A3PF achieves significant improvements over previous active methods in terms of the area under the receiver operating characteristic curve(AUC-ROC)(20.9%and 21.5%)and the area under the precision-recall curve(AUC-PR)(44.6%and 64.1%).

Multi-scale MSDT inversion based on LAI spatial knowledge

Quantitative remote sensing inversion is ill-posed. The Moderate Resolution Imaging Spectroradiometer at 250 m resolution （MODIS_250m） contains two bands. To deal with this ill-posed inversion of MODIS_250m data, we propose a framework, the Multi-scale, Multi-stage, Sample-direction Dependent, Target-decisions （Multi-scale MSDT） inversion method, based on spa- tial knowledge. First, MODIS images （1 km, 500 m, 250 m） are used to extract multi-scale spatial knowledge. The inversion accuracy of MODIS_lkm data is improved by reducing the impact of spatial heterogeneity. Then, coarse-scale inversion is taken as prior knowledge for the fine scale, again by inversion. The prior knowledge is updated after each inversion step. At each scale, MODIS_lkm to MODIS_250m, the inversion is directed by the Uncertainty and Sensitivity Matrix （USM）, and the most uncertain parameters are inversed by the most sensitive data. All remote sensing data are involved in the inversion, during which multi-scale spatial knowledge is introduced, to reduce the impact of spatial heterogeneity. The USM analysis is used to implement a reasonable allocation of limited remote sensing data in the model space. In the entire multi-scale inversion process field data, spatial knowledge and multi-scale remote sensing data are all involved. As the multi-scale, multi-stage inversion is gradually refined, initial expectations of parameters become more reasonable and their uncertainty range is effectively reduced, so that the inversion becomes increasingly targeted. Finally, the method is tested by retrieving the Leaf Area Index （LAI） of the crop canopy in the Heihe River Basin. The results show that the proposed method is reliable.

Using knowledge inference to suppress the lamp disturbance for fire detection

Fire detection in buildings is crucial for people’s lives and property.Conventional temperature and smoke sen-sors have many disadvantages:the limited cover range;detection delays;the difficulty in distinguishing smoke and fire.Recently,research on convolutional neural networks(CNN)for fire image detection has become a hot topic.However,existing fire classification and object detection methods are often interfered with by flash-lights,red objects and the high-brightness background,resulting in a high false alarm rate.Besides,light and lamps often exist in buildings.To address this issue,this paper focuses on introducing scene prior knowledge and causal inference mechanisms to suppress the lamp disturbance.Firstly,we train the YoloV3 network to detect and recognize lamps.Secondly,to reduce the dataset bias,we mask the lamp regions with the pro-posed Local Grabcut segmentation method.Last,compared with direct fire classification methods,our proposed methods reduce about 34.6%false alarm rate based on InceptionV4 networks.The experimental results verify the effectiveness among different CNN architectures(Resnet101,Firenet,Densenet121).The code is online at https://github.com/kailaisun/fire-detection-without-lamp.